{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Batch Normalization\n",
    "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. \n",
    "One idea along these lines is batch normalization which was proposed by [1] in 2015.\n",
    "\n",
    "The idea is relatively straightforward. Machine learning methods tend to work better when their input data consists of uncorrelated features with zero mean and unit variance. When training a neural network, we can preprocess the data before feeding it to the network to explicitly decorrelate its features; this will ensure that the first layer of the network sees data that follows a nice distribution. However, even if we preprocess the input data, the activations at deeper layers of the network will likely no longer be decorrelated and will no longer have zero mean or unit variance since they are output from earlier layers in the network. Even worse, during the training process the distribution of features at each layer of the network will shift as the weights of each layer are updated.\n",
    "\n",
    "The authors of [1] hypothesize that the shifting distribution of features inside deep neural networks may make training deep networks more difficult. To overcome this problem, [1] proposes to insert batch normalization layers into the network. At training time, a batch normalization layer uses a minibatch of data to estimate the mean and standard deviation of each feature. These estimated means and standard deviations are then used to center and normalize the features of the minibatch. A running average of these means and standard deviations is kept during training, and at test time these running averages are used to center and normalize features.\n",
    "\n",
    "It is possible that this normalization strategy could reduce the representational power of the network, since it may sometimes be optimal for certain layers to have features that are not zero-mean or unit variance. To this end, the batch normalization layer includes learnable shift and scale parameters for each feature dimension.\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n",
      "\tYou will need to compile a Cython extension for a portion of this assignment.\n",
      "\tThe instructions to do this will be given in a section of the notebook below.\n",
      "\tThere will be an option for Colab users and another for Jupyter (local) users.\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def print_mean_std(x,axis=0):\n",
    "    print('  means: ', x.mean(axis=axis))\n",
    "    print('  stds:  ', x.std(axis=axis))\n",
    "    print() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: forward\n",
    "In the file `cs231n/layers.py`, implement the batch normalization forward pass in the function `batchnorm_forward`. Once you have done so, run the following to test your implementation.\n",
    "\n",
    "Referencing the paper linked to above in [1] may be helpful!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before batch normalization:\n",
      "  means:  [ -2.3814598  -13.18038246   1.91780462]\n",
      "  stds:   [27.18502186 34.21455511 37.68611762]\n",
      "\n",
      "After batch normalization (gamma=1, beta=0)\n",
      "  means:  [5.32907052e-17 7.04991621e-17 1.85962357e-17]\n",
      "  stds:   [0.99999999 1.         1.        ]\n",
      "\n",
      "After batch normalization (gamma= [1. 2. 3.] , beta= [11. 12. 13.] )\n",
      "  means:  [11. 12. 13.]\n",
      "  stds:   [0.99999999 1.99999999 2.99999999]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after batch normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before batch normalization:')\n",
    "print_mean_std(a,axis=0)\n",
    "\n",
    "gamma = np.ones((D3,))\n",
    "beta = np.zeros((D3,))\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After batch normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)\n",
    "\n",
    "gamma = np.asarray([1.0, 2.0, 3.0])\n",
    "beta = np.asarray([11.0, 12.0, 13.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After batch normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After batch normalization (test-time):\n",
      "  means:  [-0.03927354 -0.04349152 -0.10452688]\n",
      "  stds:   [1.01531428 1.01238373 0.97819988]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 = 200, 50, 60, 3\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "\n",
    "for t in range(50):\n",
    "  X = np.random.randn(N, D1)\n",
    "  a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "  batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "bn_param['mode'] = 'test'\n",
    "X = np.random.randn(N, D1)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After batch normalization (test-time):')\n",
    "print_mean_std(a_norm,axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: backward\n",
    "Now implement the backward pass for batch normalization in the function `batchnorm_backward`.\n",
    "\n",
    "To derive the backward pass you should write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; make sure to sum gradients across these branches in the backward pass.\n",
    "\n",
    "Once you have finished, run the following to numerically check your backward pass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.00000000e+00  1.56125113e-17  0.00000000e+00  0.00000000e+00\n",
      "   1.04083409e-17]\n",
      " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00  0.00000000e+00\n",
      "   2.08166817e-17]\n",
      " [ 0.00000000e+00  1.73472348e-18  0.00000000e+00  0.00000000e+00\n",
      "  -1.38777878e-17]\n",
      " [ 0.00000000e+00 -2.77555756e-17  0.00000000e+00  0.00000000e+00\n",
      "  -1.38777878e-17]]\n",
      "dx error:  1.7029235612572515e-09\n",
      "dgamma error:  7.420414216247087e-13\n",
      "dbeta error:  2.8795057655839487e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: batchnorm_forward(x, a, beta, bn_param)[0]\n",
    "fb = lambda b: batchnorm_forward(x, gamma, b, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = batchnorm_backward(dout, cache)\n",
    "#You should expect to see relative errors between 1e-13 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch normalization: alternative backward\n",
    "In class we talked about two different implementations for the sigmoid backward pass. One strategy is to write out a computation graph composed of simple operations and backprop through all intermediate values. Another strategy is to work out the derivatives on paper. For example, you can derive a very simple formula for the sigmoid function's backward pass by simplifying gradients on paper.\n",
    "\n",
    "Surprisingly, it turns out that you can do a similar simplification for the batch normalization backward pass too!  \n",
    "\n",
    "In the forward pass, given a set of inputs $X=\\begin{bmatrix}x_1\\\\x_2\\\\...\\\\x_N\\end{bmatrix}$, \n",
    "\n",
    "we first calculate the mean $\\mu$ and variance $v$.\n",
    "With $\\mu$ and $v$ calculated, we can calculate the standard deviation $\\sigma$  and normalized data $Y$.\n",
    "The equations and graph illustration below describe the computation ($y_i$ is the i-th element of the vector $Y$).\n",
    "\n",
    "\\begin{align}\n",
    "& \\mu=\\frac{1}{N}\\sum_{k=1}^N x_k  &  v=\\frac{1}{N}\\sum_{k=1}^N (x_k-\\mu)^2 \\\\\n",
    "& \\sigma=\\sqrt{v+\\epsilon}         &  y_i=\\frac{x_i-\\mu}{\\sigma}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/cs231n/cs231n.github.io/master/assets/a2/batchnorm_graph.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "The meat of our problem during backpropagation is to compute $\\frac{\\partial L}{\\partial X}$, given the upstream gradient we receive, $\\frac{\\partial L}{\\partial Y}.$ To do this, recall the chain rule in calculus gives us $\\frac{\\partial L}{\\partial X} = \\frac{\\partial L}{\\partial Y} \\cdot \\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "The unknown/hart part is $\\frac{\\partial Y}{\\partial X}$. We can find this by first deriving step-by-step our local gradients at \n",
    "$\\frac{\\partial v}{\\partial X}$, $\\frac{\\partial \\mu}{\\partial X}$,\n",
    "$\\frac{\\partial \\sigma}{\\partial v}$, \n",
    "$\\frac{\\partial Y}{\\partial \\sigma}$, and $\\frac{\\partial Y}{\\partial \\mu}$,\n",
    "and then use the chain rule to compose these gradients (which appear in the form of vectors!) appropriately to compute $\\frac{\\partial Y}{\\partial X}$.\n",
    "\n",
    "If it's challenging to directly reason about the gradients over $X$ and $Y$ which require matrix multiplication, try reasoning about the gradients in terms of individual elements $x_i$ and $y_i$ first: in that case, you will need to come up with the derivations for $\\frac{\\partial L}{\\partial x_i}$, by relying on the Chain Rule to first calculate the intermediate $\\frac{\\partial \\mu}{\\partial x_i}, \\frac{\\partial v}{\\partial x_i}, \\frac{\\partial \\sigma}{\\partial x_i},$ then assemble these pieces to calculate $\\frac{\\partial y_i}{\\partial x_i}$. \n",
    "\n",
    "You should make sure each of the intermediary gradient derivations are all as simplified as possible, for ease of implementation. \n",
    "\n",
    "After doing so, implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Your two implementations should compute nearly identical results, but the alternative implementation should be a bit faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-6.93889390e-18 -1.38777878e-17  0.00000000e+00 ...  3.46944695e-18\n",
      "   0.00000000e+00 -1.38777878e-17]\n",
      " [ 0.00000000e+00 -1.38777878e-17  5.55111512e-17 ...  3.46944695e-18\n",
      "   0.00000000e+00 -6.93889390e-18]\n",
      " [ 0.00000000e+00 -1.73472348e-17 -5.20417043e-18 ...  0.00000000e+00\n",
      "   1.38777878e-17  0.00000000e+00]\n",
      " ...\n",
      " [ 0.00000000e+00 -1.38777878e-17 -3.46944695e-17 ...  3.46944695e-18\n",
      "   0.00000000e+00  0.00000000e+00]\n",
      " [-6.93889390e-18 -8.67361738e-18  0.00000000e+00 ...  2.60208521e-18\n",
      "   0.00000000e+00 -1.38777878e-17]\n",
      " [-6.93889390e-18 -6.93889390e-18  1.38777878e-17 ...  3.46944695e-18\n",
      "   0.00000000e+00 -1.38777878e-17]]\n",
      "dx difference:  7.666703093525566e-13\n",
      "dgamma difference:  0.0\n",
      "dbeta difference:  0.0\n",
      "speedup: 1.50x\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D = 100, 500\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "out, cache = batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "t1 = time.time()\n",
    "dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache)\n",
    "t2 = time.time()\n",
    "dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache)\n",
    "t3 = time.time()\n",
    "\n",
    "print('dx difference: ', rel_error(dx1, dx2))\n",
    "print('dgamma difference: ', rel_error(dgamma1, dgamma2))\n",
    "print('dbeta difference: ', rel_error(dbeta1, dbeta2))\n",
    "print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fully Connected Nets with Batch Normalization\n",
    "Now that you have a working implementation for batch normalization, go back to your `FullyConnectedNet` in the file `cs231n/classifiers/fc_net.py`. Modify your implementation to add batch normalization.\n",
    "\n",
    "Concretely, when the `normalization` flag is set to `\"batchnorm\"` in the constructor, you should insert a batch normalization layer before each ReLU nonlinearity. The outputs from the last layer of the network should not be normalized. Once you are done, run the following to gradient-check your implementation.\n",
    "\n",
    "HINT: You might find it useful to define an additional helper layer similar to those in the file `cs231n/layer_utils.py`. If you decide to do so, do it in the file `cs231n/classifiers/fc_net.py`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.2611955101340957\n",
      "W1 relative error: 1.10e-04\n",
      "W2 relative error: 2.85e-06\n",
      "W3 relative error: 4.05e-10\n",
      "b1 relative error: 4.44e-08\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 1.01e-10\n",
      "beta1 relative error: 7.33e-09\n",
      "beta2 relative error: 1.89e-09\n",
      "gamma1 relative error: 6.96e-09\n",
      "gamma2 relative error: 1.96e-09\n",
      "\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  6.996533220108303\n",
      "W1 relative error: 1.98e-06\n",
      "W2 relative error: 2.29e-06\n",
      "W3 relative error: 1.11e-08\n",
      "b1 relative error: 5.55e-09\n",
      "b2 relative error: 2.22e-08\n",
      "b3 relative error: 2.10e-10\n",
      "beta1 relative error: 6.65e-09\n",
      "beta2 relative error: 4.23e-09\n",
      "gamma1 relative error: 6.27e-09\n",
      "gamma2 relative error: 5.28e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "\n",
    "# You should expect losses between 1e-4~1e-10 for W, \n",
    "# losses between 1e-08~1e-10 for b,\n",
    "# and losses between 1e-08~1e-09 for beta and gammas.\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64,\n",
    "                            normalization='batchnorm')\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "  if reg == 0: print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batchnorm for deep networks\n",
    "Run the following to train a six-layer network on a subset of 1000 training examples both with and without batch normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver with batch norm:\n",
      "(Iteration 1 / 200) loss: 2.340974\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.115000\n",
      "(Epoch 1 / 10) train acc: 0.314000; val_acc: 0.266000\n",
      "(Iteration 21 / 200) loss: 2.039365\n",
      "(Epoch 2 / 10) train acc: 0.385000; val_acc: 0.279000\n",
      "(Iteration 41 / 200) loss: 2.041103\n",
      "(Epoch 3 / 10) train acc: 0.494000; val_acc: 0.308000\n",
      "(Iteration 61 / 200) loss: 1.753902\n",
      "(Epoch 4 / 10) train acc: 0.531000; val_acc: 0.307000\n",
      "(Iteration 81 / 200) loss: 1.246584\n",
      "(Epoch 5 / 10) train acc: 0.574000; val_acc: 0.313000\n",
      "(Iteration 101 / 200) loss: 1.320589\n",
      "(Epoch 6 / 10) train acc: 0.633000; val_acc: 0.338000\n",
      "(Iteration 121 / 200) loss: 1.159472\n",
      "(Epoch 7 / 10) train acc: 0.686000; val_acc: 0.325000\n",
      "(Iteration 141 / 200) loss: 1.156478\n",
      "(Epoch 8 / 10) train acc: 0.769000; val_acc: 0.334000\n",
      "(Iteration 161 / 200) loss: 0.630541\n",
      "(Epoch 9 / 10) train acc: 0.801000; val_acc: 0.344000\n",
      "(Iteration 181 / 200) loss: 0.859707\n",
      "(Epoch 10 / 10) train acc: 0.791000; val_acc: 0.331000\n",
      "\n",
      "Solver without batch norm:\n",
      "(Iteration 1 / 200) loss: 2.302332\n",
      "(Epoch 0 / 10) train acc: 0.129000; val_acc: 0.131000\n",
      "(Epoch 1 / 10) train acc: 0.283000; val_acc: 0.250000\n",
      "(Iteration 21 / 200) loss: 2.041970\n",
      "(Epoch 2 / 10) train acc: 0.316000; val_acc: 0.277000\n",
      "(Iteration 41 / 200) loss: 1.900473\n",
      "(Epoch 3 / 10) train acc: 0.373000; val_acc: 0.282000\n",
      "(Iteration 61 / 200) loss: 1.713156\n",
      "(Epoch 4 / 10) train acc: 0.390000; val_acc: 0.310000\n",
      "(Iteration 81 / 200) loss: 1.662208\n",
      "(Epoch 5 / 10) train acc: 0.434000; val_acc: 0.300000\n",
      "(Iteration 101 / 200) loss: 1.696062\n",
      "(Epoch 6 / 10) train acc: 0.536000; val_acc: 0.346000\n",
      "(Iteration 121 / 200) loss: 1.550785\n",
      "(Epoch 7 / 10) train acc: 0.530000; val_acc: 0.310000\n",
      "(Iteration 141 / 200) loss: 1.436307\n",
      "(Epoch 8 / 10) train acc: 0.622000; val_acc: 0.342000\n",
      "(Iteration 161 / 200) loss: 1.000868\n",
      "(Epoch 9 / 10) train acc: 0.654000; val_acc: 0.328000\n",
      "(Iteration 181 / 200) loss: 0.925455\n",
      "(Epoch 10 / 10) train acc: 0.726000; val_acc: 0.335000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [100, 100, 100, 100, 100]\n",
    "\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 2e-2\n",
    "bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "print('Solver with batch norm:')\n",
    "bn_solver = Solver(bn_model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True,print_every=20)\n",
    "bn_solver.train()\n",
    "\n",
    "print('\\nSolver without batch norm:')\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=10, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following to visualize the results from two networks trained above. You should find that using batch normalization helps the network to converge much faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_training_history(title, label, baseline, bn_solvers, plot_fn, bl_marker='.', bn_marker='.', labels=None):\n",
    "    \"\"\"utility function for plotting training history\"\"\"\n",
    "    plt.title(title)\n",
    "    plt.xlabel(label)\n",
    "    bn_plots = [plot_fn(bn_solver) for bn_solver in bn_solvers]\n",
    "    bl_plot = plot_fn(baseline)\n",
    "    num_bn = len(bn_plots)\n",
    "    for i in range(num_bn):\n",
    "        label='with_norm'\n",
    "        if labels is not None:\n",
    "            label += str(labels[i])\n",
    "        plt.plot(bn_plots[i], bn_marker, label=label)\n",
    "    label='baseline'\n",
    "    if labels is not None:\n",
    "        label += str(labels[0])\n",
    "    plt.plot(bl_plot, bl_marker, label=label)\n",
    "    plt.legend(loc='lower center', ncol=num_bn+1) \n",
    "\n",
    "    \n",
    "plt.subplot(3, 1, 1)\n",
    "plot_training_history('Training loss','Iteration', solver, [bn_solver], \\\n",
    "                      lambda x: x.loss_history, bl_marker='o', bn_marker='o')\n",
    "plt.subplot(3, 1, 2)\n",
    "plot_training_history('Training accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "plt.subplot(3, 1, 3)\n",
    "plot_training_history('Validation accuracy','Epoch', solver, [bn_solver], \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-o', bn_marker='-o')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and initialization\n",
    "We will now run a small experiment to study the interaction of batch normalization and weight initialization.\n",
    "\n",
    "The first cell will train 8-layer networks both with and without batch normalization using different scales for weight initialization. The second layer will plot training accuracy, validation set accuracy, and training loss as a function of the weight initialization scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running weight scale 1 / 20\n",
      "Running weight scale 2 / 20\n",
      "Running weight scale 3 / 20\n",
      "Running weight scale 4 / 20\n",
      "Running weight scale 5 / 20\n",
      "Running weight scale 6 / 20\n",
      "Running weight scale 7 / 20\n",
      "Running weight scale 8 / 20\n",
      "Running weight scale 9 / 20\n",
      "Running weight scale 10 / 20\n",
      "Running weight scale 11 / 20\n",
      "Running weight scale 12 / 20\n",
      "Running weight scale 13 / 20\n",
      "Running weight scale 14 / 20\n",
      "Running weight scale 15 / 20\n",
      "Running weight scale 16 / 20\n",
      "Running weight scale 17 / 20\n",
      "Running weight scale 18 / 20\n",
      "Running weight scale 19 / 20\n",
      "Running weight scale 20 / 20\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Try training a very deep net with batchnorm\n",
    "hidden_dims = [50, 50, 50, 50, 50, 50, 50]\n",
    "num_train = 1000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "bn_solvers_ws = {}\n",
    "solvers_ws = {}\n",
    "weight_scales = np.logspace(-4, 0, num=20)\n",
    "for i, weight_scale in enumerate(weight_scales):\n",
    "    print('Running weight scale %d / %d' % (i + 1, len(weight_scales)))\n",
    "    bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization='batchnorm')\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "\n",
    "    bn_solver = Solver(bn_model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    bn_solver.train()\n",
    "    bn_solvers_ws[weight_scale] = bn_solver\n",
    "\n",
    "    solver = Solver(model, small_data,\n",
    "                  num_epochs=10, batch_size=50,\n",
    "                  update_rule='adam',\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-3,\n",
    "                  },\n",
    "                  verbose=False, print_every=200)\n",
    "    solver.train()\n",
    "    solvers_ws[weight_scale] = solver"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results of weight scale experiment\n",
    "best_train_accs, bn_best_train_accs = [], []\n",
    "best_val_accs, bn_best_val_accs = [], []\n",
    "final_train_loss, bn_final_train_loss = [], []\n",
    "\n",
    "for ws in weight_scales:\n",
    "  best_train_accs.append(max(solvers_ws[ws].train_acc_history))\n",
    "  bn_best_train_accs.append(max(bn_solvers_ws[ws].train_acc_history))\n",
    "  \n",
    "  best_val_accs.append(max(solvers_ws[ws].val_acc_history))\n",
    "  bn_best_val_accs.append(max(bn_solvers_ws[ws].val_acc_history))\n",
    "  \n",
    "  final_train_loss.append(np.mean(solvers_ws[ws].loss_history[-100:]))\n",
    "  bn_final_train_loss.append(np.mean(bn_solvers_ws[ws].loss_history[-100:]))\n",
    "  \n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Best val accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best val accuracy')\n",
    "plt.semilogx(weight_scales, best_val_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_val_accs, '-o', label='batchnorm')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Best train accuracy vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Best training accuracy')\n",
    "plt.semilogx(weight_scales, best_train_accs, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_best_train_accs, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Final training loss vs weight initialization scale')\n",
    "plt.xlabel('Weight initialization scale')\n",
    "plt.ylabel('Final training loss')\n",
    "plt.semilogx(weight_scales, final_train_loss, '-o', label='baseline')\n",
    "plt.semilogx(weight_scales, bn_final_train_loss, '-o', label='batchnorm')\n",
    "plt.legend()\n",
    "plt.gca().set_ylim(1.0, 3.5)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1:\n",
    "Describe the results of this experiment. How does the scale of weight initialization affect models with/without batch normalization differently, and why?\n",
    "\n",
    "## Answer:\n",
    "当使用bn的时候较为稳定\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch normalization and batch size\n",
    "We will now run a small experiment to study the interaction of batch normalization and batch size.\n",
    "\n",
    "The first cell will train 6-layer networks both with and without batch normalization using different batch sizes. The second layer will plot training accuracy and validation set accuracy over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    }
   ],
   "source": [
    "def run_batchsize_experiments(normalization_mode):\n",
    "    np.random.seed(231)\n",
    "    # Try training a very deep net with batchnorm\n",
    "    hidden_dims = [100, 100, 100, 100, 100]\n",
    "    num_train = 1000\n",
    "    small_data = {\n",
    "      'X_train': data['X_train'][:num_train],\n",
    "      'y_train': data['y_train'][:num_train],\n",
    "      'X_val': data['X_val'],\n",
    "      'y_val': data['y_val'],\n",
    "    }\n",
    "    n_epochs=10\n",
    "    weight_scale = 2e-2\n",
    "    batch_sizes = [5,10,50]\n",
    "    lr = 10**(-3.5)\n",
    "    solver_bsize = batch_sizes[0]\n",
    "\n",
    "    print('No normalization: batch size = ',solver_bsize)\n",
    "    model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=None)\n",
    "    solver = Solver(model, small_data,\n",
    "                    num_epochs=n_epochs, batch_size=solver_bsize,\n",
    "                    update_rule='adam',\n",
    "                    optim_config={\n",
    "                      'learning_rate': lr,\n",
    "                    },\n",
    "                    verbose=False)\n",
    "    solver.train()\n",
    "    \n",
    "    bn_solvers = []\n",
    "    for i in range(len(batch_sizes)):\n",
    "        b_size=batch_sizes[i]\n",
    "        print('Normalization: batch size = ',b_size)\n",
    "        bn_model = FullyConnectedNet(hidden_dims, weight_scale=weight_scale, normalization=normalization_mode)\n",
    "        bn_solver = Solver(bn_model, small_data,\n",
    "                        num_epochs=n_epochs, batch_size=b_size,\n",
    "                        update_rule='adam',\n",
    "                        optim_config={\n",
    "                          'learning_rate': lr,\n",
    "                        },\n",
    "                        verbose=False)\n",
    "        bn_solver.train()\n",
    "        bn_solvers.append(bn_solver)\n",
    "        \n",
    "    return bn_solvers, solver, batch_sizes\n",
    "\n",
    "batch_sizes = [5,10,50]\n",
    "bn_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('batchnorm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Batch Normalization)','Epoch', solver_bsize, bn_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "Describe the results of this experiment. What does this imply about the relationship between batch normalization and batch size? Why is this relationship observed?\n",
    "\n",
    "## Answer:\n",
    "[收敛更快]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization\n",
    "Batch normalization has proved to be effective in making networks easier to train, but the dependency on batch size makes it less useful in complex networks which have a cap on the input batch size due to hardware limitations. \n",
    "\n",
    "Several alternatives to batch normalization have been proposed to mitigate this problem; one such technique is Layer Normalization [2]. Instead of normalizing over the batch, we normalize over the features. In other words, when using Layer Normalization, each feature vector corresponding to a single datapoint is normalized based on the sum of all terms within that feature vector.\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 3:\n",
    "Which of these data preprocessing steps is analogous to batch normalization, and which is analogous to layer normalization?\n",
    "\n",
    "1. Scaling each image in the dataset, so that the RGB channels for each row of pixels within an image sums up to 1.\n",
    "2. Scaling each image in the dataset, so that the RGB channels for all pixels within an image sums up to 1.  \n",
    "3. Subtracting the mean image of the dataset from each image in the dataset.\n",
    "4. Setting all RGB values to either 0 or 1 depending on a given threshold.\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization: Implementation\n",
    "\n",
    "Now you'll implement layer normalization. This step should be relatively straightforward, as conceptually the implementation is almost identical to that of batch normalization. One significant difference though is that for layer normalization, we do not keep track of the moving moments, and the testing phase is identical to the training phase, where the mean and variance are directly calculated per datapoint.\n",
    "\n",
    "Here's what you need to do:\n",
    "\n",
    "* In `cs231n/layers.py`, implement the forward pass for layer normalization in the function `layernorm_forward`. \n",
    "\n",
    "Run the cell below to check your results.\n",
    "* In `cs231n/layers.py`, implement the backward pass for layer normalization in the function `layernorm_backward`. \n",
    "\n",
    "Run the second cell below to check your results.\n",
    "* Modify `cs231n/classifiers/fc_net.py` to add layer normalization to the `FullyConnectedNet`. When the `normalization` flag is set to `\"layernorm\"` in the constructor, you should insert a layer normalization layer before each ReLU nonlinearity. \n",
    "\n",
    "Run the third cell below to run the batch size experiment on layer normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before layer normalization:\n",
      "  means:  [-59.06673243 -47.60782686 -43.31137368 -26.40991744]\n",
      "  stds:   [10.07429373 28.39478981 35.28360729  4.01831507]\n",
      "\n",
      "After layer normalization (gamma=1, beta=0)\n",
      "(4, 3)\n",
      "(1, 3)\n",
      "(4, 1)\n",
      "  means:  [ 4.81096644e-16 -7.40148683e-17  2.22044605e-16 -5.92118946e-16]\n",
      "  stds:   [0.99999995 0.99999999 1.         0.99999969]\n",
      "\n",
      "After layer normalization (gamma= [3. 3. 3.] , beta= [5. 5. 5.] )\n",
      "(4, 3)\n",
      "(1, 3)\n",
      "(4, 1)\n",
      "  means:  [5. 5. 5. 5.]\n",
      "  stds:   [2.99999985 2.99999998 2.99999999 2.99999907]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after layer normalization   \n",
    "\n",
    "# Simulate the forward pass for a two-layer network\n",
    "np.random.seed(231)\n",
    "N, D1, D2, D3 =4, 50, 60, 3\n",
    "X = np.random.randn(N, D1)\n",
    "W1 = np.random.randn(D1, D2)\n",
    "W2 = np.random.randn(D2, D3)\n",
    "a = np.maximum(0, X.dot(W1)).dot(W2)\n",
    "\n",
    "print('Before layer normalization:')\n",
    "print_mean_std(a,axis=1)\n",
    "\n",
    "gamma = np.ones(D3)\n",
    "beta = np.zeros(D3)\n",
    "# Means should be close to zero and stds close to one\n",
    "print('After layer normalization (gamma=1, beta=0)')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)\n",
    "\n",
    "gamma = np.asarray([3.0,3.0,3.0])\n",
    "beta = np.asarray([5.0,5.0,5.0])\n",
    "# Now means should be close to beta and stds close to gamma\n",
    "print('After layer normalization (gamma=', gamma, ', beta=', beta, ')')\n",
    "a_norm, _ = layernorm_forward(a, gamma, beta, {'mode': 'train'})\n",
    "print_mean_std(a_norm,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 1)\n",
      "dx error:  1.433615146847572e-09\n",
      "dgamma error:  4.519489546032799e-12\n",
      "dbeta error:  2.276445013433725e-12\n"
     ]
    }
   ],
   "source": [
    "# Gradient check batchnorm backward pass\n",
    "np.random.seed(231)\n",
    "N, D = 4, 5\n",
    "x = 5 * np.random.randn(N, D) + 12\n",
    "gamma = np.random.randn(D)\n",
    "beta = np.random.randn(D)\n",
    "dout = np.random.randn(N, D)\n",
    "\n",
    "ln_param = {}\n",
    "fx = lambda x: layernorm_forward(x, gamma, beta, ln_param)[0]\n",
    "fg = lambda a: layernorm_forward(x, a, beta, ln_param)[0]\n",
    "fb = lambda b: layernorm_forward(x, gamma, b, ln_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma.copy(), dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta.copy(), dout)\n",
    "\n",
    "_, cache = layernorm_forward(x, gamma, beta, ln_param)\n",
    "dx, dgamma, dbeta = layernorm_backward(dout, cache)\n",
    "\n",
    "#You should expect to see relative errors between 1e-12 and 1e-8\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Layer Normalization and batch size\n",
    "\n",
    "We will now run the previous batch size experiment with layer normalization instead of batch normalization. Compared to the previous experiment, you should see a markedly smaller influence of batch size on the training history!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No normalization: batch size =  5\n",
      "Normalization: batch size =  5\n",
      "Normalization: batch size =  10\n",
      "Normalization: batch size =  50\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ln_solvers_bsize, solver_bsize, batch_sizes = run_batchsize_experiments('layernorm')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_training_history('Training accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.train_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_training_history('Validation accuracy (Layer Normalization)','Epoch', solver_bsize, ln_solvers_bsize, \\\n",
    "                      lambda x: x.val_acc_history, bl_marker='-^', bn_marker='-o', labels=batch_sizes)\n",
    "\n",
    "plt.gcf().set_size_inches(15, 10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 4:\n",
    "When is layer normalization likely to not work well, and why?\n",
    "\n",
    "1. Using it in a very deep network\n",
    "2. Having a very small dimension of features\n",
    "3. Having a high regularization term\n",
    "\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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